Predictive Processing Symposium: From Concept to Circuits

Introduction to Predictive Processing starting at 09:00 ONLINE 

Symposium starting at 12:00 ONLINE

Kleiner Festsaal
University of Vienna
Main Building, Universitätsring 1 

Please note that registration is mandatory.

Please register separately for the introductory lectures (online) and for the symposium.

Please, note due to the current regulations of the Ministry of Health we decided that the symposium will take place online only.


The Vienna CogSciHub is a research network emerging from the longstanding Research Platform Cognitive Science to realize the interdisciplinary development of joining Cognitive Science and Neuroscience at the University of Vienna. 

All students, researchers and people interested in the field of Cognitive Science and interdisciplinary, multimodal research are most welcome to join our annual symposium. Our focus this year will be Predictive Processing and Neuroscience. 

Registration is mandatory! Entrance free!



  • 09:00-10:00 | Introduction lecture 1 - Ronald Sladky, University of Vienna, Social, Cognitive & Affective Neuroscience Unit (online, please register separately for this lecture)
  • 10:00-11:00 | Introduction lecture 2 - Moritz Grosse-Wentrup, University of Vienna, Head of the Research Group Neuroinformatics (online, please register separately for this lecture)


  • 12:00 | OPENING SPEECH
    Introducing Words Rector Heinz W. Engl, University of Vienna

  • 12:10 | WELCOME
    Short welcome address by Helmut Leder, Head of the Vienna CogSciHub


  • 12:20-13:20 | "ME AND MY MARKOV BLANKET" presented by Karl J. Friston, 
    Scientific Director: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL

    13:20-13:50 | Question & Answers 

  • 14:00-15:00 | "WHAT DO DOPAMINE NEURONS COMPUTE" presented by Naoshige Uchida
    Harvard University | Center for Brain Science, Department of Molecular and Cellular Biology 

    15:00-15:30 | Question & Answers

  • 15:30-16:00 | HOME COFFEE BREAK

  • 16:00-17:00 | PANEL DISCUSSION with
    Karl J. Friston (University College London)
    Naoshige Uchida (Harvard University)
    Isabella Sarto-Jackson (Konrad Lorenz Institute for Evolution and Cognition Research)
    Moritz Grosse-Wentrup (University of Vienna)
    Manuel Zimmer (University of Vienna)
    Anchorman and scientific moderation: Ronald Sladky (University of Vienna)

  • 17:00 | END


Professor Karl J. Friston MB, BS, MA, MRCPsych, FMedSci, FRSB, FRS

Wellcome Principal Fellow
Scientific Director: Wellcome Trust Centre for Neuroimaging
Institute of Neurology, UCL
12 Queen Square
London. WC1N 3BG UK

Keynote title


This presentation offers a heuristic proof (and simulations of a primordial soup) suggesting that life—or biological self-organization—is an inevitable and emergent property of any (weakly mixing) random dynamical system that possesses a Markov blanket. This conclusion is based on the following arguments: if a system can be differentiated from its external milieu, heat bath or environment, then the system’s internal and external states must be conditionally independent. These independencies induce a Markov blanket that separates internal and external states. This separation means that internal states will appear to minimize a free energy functional of blanket states – via a variational principle of stationary action. Crucially, this equips internal states with an information geometry, pertaining to probabilistic beliefs about something; namely external states. Interestingly, this free energy is the same quantity that is optimized in Bayesian inference and machine learning (where it is known as an evidence lower bound). In short, internal states (and their Markov blanket) will appear to model—and act on—their world to preserve their functional and structural integrity. This leads to a Bayesian mechanics, which can be neatly summarised as self-evidencing.


Key words: active inference ∙ autopoiesis ∙ cognitive ∙ dynamics ∙ free energy ∙ epistemic value ∙ self-organization.


Karl Friston is a theoretical neuroscientist and authority on brain imaging. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). These contributions were motivated by schizophrenia research and theoretical studies of value-learning, formulated as the dysconnection hypothesis of schizophrenia. Mathematical contributions include variational Laplacian procedures and generalized filtering for hierarchical Bayesian model inversion. Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference). Friston received the first Young Investigators Award in Human Brain Mapping (1996) and was elected a Fellow of the Academy of Medical Sciences (1999). In 2000 he was President of the international Organization of Human Brain Mapping. In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of the Royal Society in 2006. In 2008 he received a Medal, College de France and an Honorary Doctorate from the University of York in 2011. He became of Fellow of the Royal Society of Biology in 2012, received the Weldon Memorial prize and Medal in 2013 for contributions to mathematical biology and was elected as a member of EMBO (excellence in the life sciences) in 2014 and the Academia Europaea in (2015). He was the 2016 recipient of the Charles Branch Award for unparalleled breakthroughs in Brain Research and the Glass Brain Award, a lifetime achievement award in the field of human brain mapping. He holds Honorary Doctorates from the University of Zurich and Radboud University.


Professor Naoshige Uchida, Ph.D.

Harvard University | Center for Brain Science
Department of Molecular and Cellular Biology
Biolabs 4057, 16 Divinity Avenue,
Cambridge, MA 02138

Keynote title


Dopamine plays a crucial role in learning from trial and error but how dopamine functions in the brain remains hotly debated. It has been proposed that the activity of dopamine neurons approximates temporal difference prediction errors (TD errors), a type of prediction errors used in reinforcement learning algorithms. In TD learning algorithms, as an agent traverses across different states, the agent updates the value of each state when it detects a change in values across consecutive time points without waiting for a final outcome. That is, it updates a guess (an estimated value of a given state) based on guesses (the difference in estimated values of consecutive time points)(Sutton and Barto, 1998). This property --“bootstrapping” values -- allows the agent to infer which actions were good even before obtaining a final outcome, and plays an essential role in solving a so-called credit assignment problem. If dopamine represents TD errors in the brain, the activity of dopamine neurons should track moment-by-moment changes in value but this idea has not been tested previously. In this talk, I will present multiple lines of evidence that demonstrate that dopamine neurons perform derivative-like computation over values on a moment-by-moment basis, instantiating a key feature of TD errors. 

Key words: reinforcement learning, dopamine, prediction error, learning algorithm, credit assignment


Naoshige Uchida is a professor at the Center for Brain Science and Department of Molecular and Cellular Biology at Harvard University. He received his Ph.D. on his study on the molecular mechanism of synaptic adhesions done in Masatoshi Takeichi‘s laboratory at Kyoto University, Japan. He started studies of olfactory coding in Kensaku Mori‘s laboratory at the Brain Science Institute, RIKEN, Japan. He then joined Zachary F. Mainen‘s laboratory at Cold Spring Harbor Laboratory, New York, USA, where he developed psychophysical olfactory decision tasks in rodents. He started his laboratory at Harvard University in 2006.

His current research focuses on the neurobiology of decision-making and learning. The research topics include the neural computation in the midbrain dopamine system, functions of the cortico-basal ganglia circuit, foraging decisions, motor learning, and artificial intelligence. His research combines quantitative rodent behaviors with multi-neuronal recordings, two-photon microscopy, computational modeling, and modern tools such as optogenetics and viral neural circuit tracing.